17:15 〜 18:30
[ACG36-P04] GOSAT-2/TANSO-CAI-2データを用いたエアロゾル導出と地上観測との比較
キーワード：エアロゾル、衛星リモートセンシング
Aerosol particles in the atmosphere scatter and absorb sunlight and affect Earth’s climate. Satellite remote sensing is one of useful way to monitor the global distribution of aerosol particle. We have developed aerosol retrieval algorithm called MWPM (Multiple wavelengths and pixels method) (Hashimoto and Nakajima, 2017). The method simultaneously retrieves fine and coarse mode AOT and single scattering albedo (SSA) by using several wavelengths and spatial difference of surface reflectance. The method is useful for aerosol retrieval over spatially inhomogeneous surface region like an urban area.
The Greenhouse Gases Observing Satellite-2 (GOSAT-2) called “Ibuki-2” was launched on October 29th, 2019 for monitoring greenhouse gases in the atmosphere from space. GOSAT-2 has two sensors, TANSO-FTS-2 (Thermal And Near-infrared Sensor for carbon Observation, Fourier Transform Spectrometer 2) and TANSO-CAI-2 (Cloud and Aerosol Imager 2). CAI-2 makes an observation at ten bands composed of seven wavelengths at 340, 380, 443, 550, 674, 869 and 1630 nm and performs two-directional observation in forward and backward directions. CAI-2 is characterized by having two ultraviolet (UV) bands that have sensitivity to light absorption by aerosol particles. The spatial resolution (IFOV) is 460 m other than a wavelength at 1630nm which resolution is 920 m.
We have applied our algorithm, MWPM, to CAI-2 data, and deriving and calculating aerosol properties such as fine and coarse mode of AOT, soot volume fraction in fine mode particles, SSA, Angstrom exponent (AE) and an equivalent value of PM2.5. The results were compared with aerosol properties of ground-based observations at sites in SKYNET and AERONET which are famous ground-based networks for aerosol monitoring.
We also have investigated biases on the products by input data and assumed aerosol models in the algorithm such as ground surface albedo, relative humidity and aerosol size distribution and so on. Aerosol particle size and these light absorption properties are different in different regions. It is important to know a bias occurred by assumed aerosol models when we validate the aerosol products by ground-based observations and modify the algorithm. We show the results of the comparisons and discuss about the results and algorithm to improve the aerosol retrieval and the algorithm.
The Greenhouse Gases Observing Satellite-2 (GOSAT-2) called “Ibuki-2” was launched on October 29th, 2019 for monitoring greenhouse gases in the atmosphere from space. GOSAT-2 has two sensors, TANSO-FTS-2 (Thermal And Near-infrared Sensor for carbon Observation, Fourier Transform Spectrometer 2) and TANSO-CAI-2 (Cloud and Aerosol Imager 2). CAI-2 makes an observation at ten bands composed of seven wavelengths at 340, 380, 443, 550, 674, 869 and 1630 nm and performs two-directional observation in forward and backward directions. CAI-2 is characterized by having two ultraviolet (UV) bands that have sensitivity to light absorption by aerosol particles. The spatial resolution (IFOV) is 460 m other than a wavelength at 1630nm which resolution is 920 m.
We have applied our algorithm, MWPM, to CAI-2 data, and deriving and calculating aerosol properties such as fine and coarse mode of AOT, soot volume fraction in fine mode particles, SSA, Angstrom exponent (AE) and an equivalent value of PM2.5. The results were compared with aerosol properties of ground-based observations at sites in SKYNET and AERONET which are famous ground-based networks for aerosol monitoring.
We also have investigated biases on the products by input data and assumed aerosol models in the algorithm such as ground surface albedo, relative humidity and aerosol size distribution and so on. Aerosol particle size and these light absorption properties are different in different regions. It is important to know a bias occurred by assumed aerosol models when we validate the aerosol products by ground-based observations and modify the algorithm. We show the results of the comparisons and discuss about the results and algorithm to improve the aerosol retrieval and the algorithm.